Bibliographic record
Abstract
[Dr. Forbes replies] In their paper,1 Forbes and associates evaluate the benefit of long-term anticoagulation after thromboembolectomy in patients without either atrial fibrillation or a cardiac thrombus. In this observational study, with retrospective and prospective components, 3 aspects of study design and data analysis deserve to be addressed. First, not all patients in the study used anticoagulation on a long-term basis. At the time of follow-up, only 79% of patients with atrial fibrillation or a cardiac thrombus (group 1) and 39% of patients without these conditions (group 2) were still taking anticoagulants. Therefore, the groups included a mix of long-term and short- term users of warfarin, and this mix was different in the 2 groups. Such a mix could have introduced a statistical bias in the comparisons by causing a dilution of the effect being studied. Second, in observational studies like this one, it is imperative that the 2 groups be comparable, except for the risk factor under study. In this study, group 2 includes 10 patients (out of 31) with malignant disease, whereas group 1 includes none. Cancer patients are at higher risk of death and thrombotic disorders.2 They should therefore be excluded to make group 2 more comparable to group 1. Finally, the outcome of amputation described in Table 3 occurred in 4 patients who “underwent lower extremity amputation during the initial hospitalization for acute ischemia.” Therefore, this outcome did not occur during follow-up but rather before the exposure being studied (warfarin treatment at the time of discharge). The epidemiologic principle of directionality requires that the outcome be observed after the exposure, so these subjects should not have been included in the analysis. To reduce these sources of bias, it would be helpful if the data could be shown for the 2 groups after removing from group 2 the 10 patients with malignant disease and the 4 patients who underwent an amputation during the initial hospitalization. Showing the results only for the long-term users of anticoagulation treatment would be also useful. Daniel Suissa, BSc Medical student Montreal, Que.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".